The current state-of-the-art of natural language processors use tree-adjoining grammars and other types of computationally-intensive processes that introduce errors because of their underlying theory. Also, current natural language processors are not suitable for small footprints because they require many libraries and other reference files. In addition, current natural language processors also require a large amount of computational power, which prevents current natural language processors from being cost-effective for small businesses having the need to use natural language products.